A Framework for Arabic Tweets Multi-label Classification Using Word Embedding and Neural Networks Algorithms

In today's world, news outlets have changed dramatically; newspapers are obsolete, and radio is no longer in the picture. People look for news online and on social media, such as Twitter and Facebook. Social media contributors share information and trending stories before verifying their truthfulness, thus, spreading rumors. Early identification of rumors from social media has attracted many researchers. However, a relatively smaller number of studies focused on other languages, such as Arabic. In this study, an Arabic rumor detection model is proposed. The model was built using transformer-based deep learning architecture. According to the literature, transformers are neural networks with outstanding performance in natural language processing tasks. Two transformers-based models, AraBERT and MARBERT, were employed, tested, and evaluated using three recently developed Arabic datasets. These models are extensions to the BERT, Bidirectional Encoder Representations from Transformers, a deep learning model that uses transformer architecture to learn the text representations and leverages the attention mechanism. We have also mitigated the challenges introduced by the imbalanced training datasets by employing two sampling techniques. The experimental results of our proposed approaches achieved 0.97 accuracy. This result demonstrated the effectiveness of the proposed method and outperformed other existing Arabic rumor detection methods.

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“…Some studies adopted DL approaches, such as [44]. The study proposed a multi-label classification framework for Arabic text on Twitter.…”

Section: B Arabic Text Classificationmentioning

confidence: 99%

Arabic Rumor Detection Using Contextual Deep Bidirectional Language Modeling

et al. 2022

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“…Klasifikasi skenario ketiga, 35 dari 40 percobaan dan untuk klasifikasi tahap kedua menunjukkan bahwa LSTM memiliki akurasi terbaik dari semua pasangan kombinasi yang tersedia. Hal ini karena LSTM tidak mengabaikan informasi semantik dari urutan kata sebelumnya dan menyimpan informasi tersebut dalam lapisan tersembunyi (Bdeir & Ibrahim, 2020). Menurut (Yan, Wang, Chun, & Yin, 2017) metode LSTM lebih unggul apabila digunakan untuk model time series.…”

Section: Pembahasanunclassified

“…Penelitian sebelumnya (Sari et al, 2020) menggunakan word2vec untuk klasifikasi multi-label dan mendapatkan hasil yang lebih baik daripada menggunakan BOW. Keunggulan word2vec adalah dapat membuat vektorisasi kata menggunakan dua teknik utama yaitu Skip-gram (SK) dan Continuous Bag of Word (CBOW) (Bdeir & Ibrahim, 2020). Dalam penelitian lain yang dilakukan (Juanlin Hu, Xin Kang, & Shun Nishide, 2019) melakukan klasifikasi multi-label dengan memanfaatkan fastText dalam mengklasifikasikan artikel berita.…”

unclassified

Deteksi Kejadian Lalu Lintas pada Teks Twitter dengan Pendekatan Klasidikasi Multi-Label Berbasis Deep Learning

Atikah

Purwitasari²,

Suciati³

2022

JTIIK

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Kemacetan merupakan salah satu kejadian yang sering terjadi di kota-kota besar. Hal ini dapat merugikan pengguna jalan, oleh karena itu perlu dilakukan pendeteksian kejadian lalu lintas. Saat ini, twitter digunakan sebagai sumber informasi untuk mendeteksi suatu kejadian. Namun, pengguna twitter cenderung membagikan beberapa informasi sekaligus, sehingga dalam satu tweet bisa memiliki lebih dari satu label. Pada penelitian ini dilakukan klasifikasi multi-label menggunakan 18.000 data dari akun twitter terverifikasi di Surabaya. Klasifikasi multi-label pada penelitian ini dilakukan untuk mengidentifikasi banyak situasi lalu lintas seperti kondisi cuaca, kecelakaan lalu lintas, kemacetan lalu lintas, lalu lintas padat, dan lalu lintas lancar. Klasifikasi dilakukan dengan menggunakan pendekatan deep learning (CNN dan LSTM) dan word embedding (word2vec dan fastText) dengan augmentasi dan non-augmentasi data. Eksperimen dilakukan dengan 3 skenario berbeda untuk melihat pengaruh data uji yang berbeda pada data latih yang sama. Selanjutnya dilakukan eksperimen untuk menguji pengaruh jumlah label terhadap klasifikasi multi-label pada data uji yang sama. Akurasi tertinggi pada non-augmentasi data adalah 0,75 dan pada augmentasi data adalah 0,95. Dari keseluruhan ujicoba akurasi tertinggi diperoleh dari kombinasi LSTM dan fastText.

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“…Pedoman PICO sangat berguna dan memudahkan dalam merumuskan pertanyaan penelitian [20]. Tabel Dataset Peneliti Jumlah Twitter [33], [34], [35], [36], [23], [24], [37], [38], [39], [22], [40], [41], [42], [43], [44], [45], [46] 17 Berita berbahasa Arab [28], [47], [48], [49], [27], [25], [50], [51] 8…”

Section: Tahap Pembuatan Rencana Awalunclassified

“…Tabel 6. Analisis Metode Representasi Teks dalam Arabic Natural Language Processing Metode Untuk Representasi Teks Peneliti Jumlah TF-IDF [4], [9], [48], [30], [23], [24], [29], [53], [54], [7], [38], [55], [32], [56], [25], [31], [59], [1], [51], [60] 20 Word2Vec [34], [24], [49], [7], [57], [39], [41], [43], [45], [46] 10 AraVec [34], [35], [37], [40], [41], [42] 6 FastText [34], [47], [35], [57], [41], [42], [50], [46] 8 mBERT [27], [22], [41], [46] 4 AraBERT [36],…”

Section: Tahap Pembuatan Rencana Awalmentioning

confidence: 99%

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Ariyanto

fatichah²,

Arifin³

2021

RESTI

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Hadith is the second source of reference for Islamic law after the Qur'an, which explains the sentences in the Qur'an which are still global by referring to the provisions of the Prophet Muhammad SAW. Classification of text documents can also be used to overcome the problem of interrelation between the Qur'an and hadith. The problem of interrelation between books of hadith needs to be done because some hadiths in certain hadith books have the same meaning as other hadith books. This study aims to analyze the development of text representation and classification methods suitable to overcome similarity meaning problems in detecting interrelationships between hadith books. The research method used is Systematic Literature Review (SLR) sourced from Google Scholar, Science Direct, and IEEE. There are 42 pieces of literature that have been studied successfully. The results showed that contextual embedding as the newest text representation method considered word context and sentence meaning better than static embedding. As a classification method, the ensemble method has better performance in classifying text documents than using only a single classifier model. Thus, future research can consider using a combination of contextual embedding and ensemble methods to detect interrelationships between books of hadith.

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A Framework for Arabic Tweets Multi-label Classification Using Word Embedding and Neural Networks Algorithms

Cited by 10 publications

References 21 publications

Arabic Rumor Detection Using Contextual Deep Bidirectional Language Modeling

Arabic Rumor Detection Using Contextual Deep Bidirectional Language Modeling

Deteksi Kejadian Lalu Lintas pada Teks Twitter dengan Pendekatan Klasidikasi Multi-Label Berbasis Deep Learning

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

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